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// //////////////////////////////////////////////////////////////////////////////////////
// Author: Sajjad Taheri, University of California, Irvine. sajjadt[at]uci[dot]edu
//
//                             LICENSE AGREEMENT
// Copyright (c) 2015 The Regents of the University of California (Regents)
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// modification, are permitted provided that the following conditions are met:
// 1. Redistributions of source code must retain the above copyright
//    notice, this list of conditions and the following disclaimer.
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//    notice, this list of conditions and the following disclaimer in the
//    documentation and/or other materials provided with the distribution.
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//    names of its contributors may be used to endorse or promote products
//    derived from this software without specific prior written permission.
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if (typeof module !== 'undefined' && module.exports) {
    // The envrionment is Node.js
    var cv = require('./opencv.js'); // eslint-disable-line no-var
    cv.FS_createLazyFile('/', 'haarcascade_frontalface_default.xml', // eslint-disable-line new-cap
                         'haarcascade_frontalface_default.xml', true, false);
}

QUnit.module('Object Detection', {});
QUnit.test('Cascade classification', function(assert) {
    // Group rectangle
    {
        let rectList = new cv.RectVector();
        let weights = new cv.IntVector();
        let groupThreshold = 1;
        const eps = 0.2;

        let rect1 = new cv.Rect(1, 2, 3, 4);
        let rect2 = new cv.Rect(1, 4, 2, 3);

        rectList.push_back(rect1);
        rectList.push_back(rect2);

        cv.groupRectangles(rectList, weights, groupThreshold, eps);


        rectList.delete();
        weights.delete();
    }

    // CascadeClassifier
    {
        let classifier = new cv.CascadeClassifier();
        const modelPath = '/haarcascade_frontalface_default.xml';

        assert.equal(classifier.empty(), true);


        classifier.load(modelPath);
        assert.equal(classifier.empty(), false);

        let image = cv.Mat.eye({height: 10, width: 10}, cv.CV_8UC3);
        let objects = new cv.RectVector();
        let numDetections = new cv.IntVector();
        const scaleFactor = 1.1;
        const minNeighbors = 3;
        const flags = 0;
        const minSize = {height: 0, width: 0};
        const maxSize = {height: 10, width: 10};

        classifier.detectMultiScale2(image, objects, numDetections, scaleFactor,
                                     minNeighbors, flags, minSize, maxSize);

        // test default parameters
        classifier.detectMultiScale2(image, objects, numDetections, scaleFactor,
                                     minNeighbors, flags, minSize);
        classifier.detectMultiScale2(image, objects, numDetections, scaleFactor,
                                     minNeighbors, flags);
        classifier.detectMultiScale2(image, objects, numDetections, scaleFactor,
                                     minNeighbors);
        classifier.detectMultiScale2(image, objects, numDetections, scaleFactor);

        classifier.delete();
        objects.delete();
        numDetections.delete();
    }

    // HOGDescriptor
    {
        let hog = new cv.HOGDescriptor();
        let mat = new cv.Mat({height: 10, width: 10}, cv.CV_8UC1);
        let descriptors = new cv.FloatVector();
        let locations = new cv.PointVector();


        assert.equal(hog.winSize.height, 128);
        assert.equal(hog.winSize.width, 64);
        assert.equal(hog.nbins, 9);
        assert.equal(hog.derivAperture, 1);
        assert.equal(hog.winSigma, -1);
        assert.equal(hog.histogramNormType, 0);
        assert.equal(hog.nlevels, 64);

        hog.nlevels = 32;
        assert.equal(hog.nlevels, 32);

        hog.delete();
        mat.delete();
        descriptors.delete();
        locations.delete();
    }
});
